Persistent homology characterizes connected components, holes, and higher-dimensional features in data across scales [1]}, and interpreting topological summaries can involve choices and hyper-parameters that are application specific. Recently McGuirl et al. [2]} developed a pipeline for interpreting information from persistent homology as descriptions of spots and stripes in an agent-based model of zebrafish patterns [3]}. The quantification results [2]} involve several hyper-parameters, which are motivated by length scales in the model [3]}. A related issue is that persistent homology is sensitive to outliers [6]}, [7]}, and this is challenging in biological settings, where noise is inherent. This raises questions about how McGuirl et al.'s approach can be adapted to quantify zebrafish patterns in noisier in vivo settings or alternative in silico settings, and about how sensitive the methods [2]} are to the choice of hyper-parameters when interpreting topological summaries.
